import numpy as np
from common.functions import *

class Relu:
    def __init__(self):
        self.mask = None 
    
    def forward(self,x):
        self.mask = (x<=0)
        out = x.copy()
        out[self.mask] = 0

        return out 
    
    def backward(self,dout):
        dout[self.mask] = 0
        dx = dout

        return dx 
    
class Sigmoid:
    def __init__(self):
        self.out = None

    def forward(self,x):
        out = sigmoid(x)
        self.out = out 
        return out 
    
    def backward(self,dout):
        dx = dout *(1.0-self.out)*self.out 
        return dx 

class Affine:
    def __init__(self,W,b):
        self.W = W 
        self.b = b
        
        self.x = None
        self.original_x_shape = None 

        self.dW = None
        self.db = None

    def forward(self,x):
        self.original_x_shape = x.shape
        x = x.reshape(x.shape[0],-1)
        self.x = x

        out = np.dot(self.x,self.W) + self.b 

        return out 

    def backward(self,dout):
        dx = np.dot(dout,self.W.T)
        self.dW = np.dot(self.x.T,dout)
        self.db = np.sum(dout,axis=0)

        dx = dx.reshape(*self.original_x_shape)
        return dx 

class SoftmaxWithLoss:
    def __init__(self):
        self.loss = None
        self.y = None # softmax的输出
        self.t = None # 监督数据

    def forward(self, x, t):
        self.t = t
        self.y = softmax(x)
        self.loss = cross_entropy_error(self.y, self.t)
        
        return self.loss

    def backward(self, dout=1):
        batch_size = self.t.shape[0]
        if self.t.size == self.y.size: # 监督数据是one-hot-vector的情况
            dx = (self.y - self.t) / batch_size
        else:
            dx = self.y.copy()
            dx[np.arange(batch_size), self.t] -= 1
            dx = dx / batch_size
        
        return dx

class BatchNormalization:
    """
    http://arxiv.org/abs/1502.03167
    """
    def __init__(self,gamma,beta,momentum=0.9,running_mean=None,running_var=None):
        self.gamma = gamma
        self.beta = beta 
        self.momentum = momentum
        self.input_shape = None # Conv层的情况下为4维，全连接层的情况下为为2维

        # 测试时使用的平均值和方差
        self.running_mean = running_mean
        self.running_var = running_var

        # backward时使用的中间数据
        self.batch_size = None 
        self.xc = None 
        self.std = None
        self.dgamma = None 
        self.dbeta = None

    def forward(self,x,train_flg=True):
        self.input_shape = x.shape
        if x.ndim != 2:
            N,C,H,W = x.shape
            x=x.reshape(N,-1)
        out = self.__forward(x,train_flg)
        return out.reshape(*self.input_shape)
    
    def __forward(self,x,train_flg):
        if self.running_mean is None:
            N,D = x.shape
            self.running_mean = np.zeros(D)
            self.running_var = np.zeros(D)

        if train_flg:
            mu = x.mean(axis=0)
            xc = x-mu
            var = np.mean(xc**2,axis=0)
            std = np.sqrt(var+10e-7)
            xn = xc/std

            self.batch_size = x.shape[0]
            self.xc = xc 
            self.xn = xn
            self.std = std 
            self.running_mean = self.momentum*self.running_mean+(1-self.momentum)*mu 
            self.running_var = self.momentum*self.running_var+(1-self.momentum)*var 
        else:
            xc = x-self.running_mean
            xn = xc/((np.sqrt(self.running_var+10e-7)))
        out = self.gamma*xn +self.beta
        return out 
    
    def backward(self,dout):
        if dout.ndim !=2:
            N,C,H,W = dout.shape
            dout = dout.reshape(N,-1)
        dx = self.__backward(dout)
        dx = dx.reshape(*self.input_shape)
        return dx 
    def __backward(self, dout):
        dbeta = dout.sum(axis=0)
        dgamma = np.sum(self.xn * dout, axis=0)
        dxn = self.gamma * dout
        dxc = dxn / self.std
        dstd = -np.sum((dxn * self.xc) / (self.std * self.std), axis=0)
        dvar = 0.5 * dstd / self.std
        dxc += (2.0 / self.batch_size) * self.xc * dvar
        dmu = np.sum(dxc, axis=0)
        dx = dxc - dmu / self.batch_size
        
        self.dgamma = dgamma
        self.dbeta = dbeta
        
        return dx
